We first consider an MIMD multiprocessor configuration with n processors. A parallel program, consisting of n processes, is executed on this system-one process per processor. The program terminates when all processes are completed. Due to synchronizations, processes may be blocked waiting for events in other processes. Associated with the program is a parallel profile vector nu , index i (1<or=i<or=n) in this vector indicates the percentage of the total execution time when i processes are executing. We then consider a distributed MIMD supercomputer with k clusters, containing u processors each. The same parallel program, consisting of n processes, is executed on this system. Each process can only be executed by processors in the same cluster. Finding a schedule with minimal completion time in this case is NP-hard. We are interested in the gain of using n processors compared to using k clusters containing u processors each. The gain is defined by the ratio between the minimal completion time using processor clusters and the completion time using a schedule with one process per processor. We present the optimal upper bound for this ratio in the form of an analytical expression in n, nu , k and u. We also demonstrate how this result can be used when evaluating heuristic scheduling algorithms (12 Refs.)